298 research outputs found

    The effects of stand characteristics on the understory vegetation in Quercus petraea and Q. cerris dominated forests

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    The shelterwood system used in Hungary has many effects on the composition and structure of the herb layer. The aim of our study was to identify the main variables that affect the occurence of herbs and seedlings in Turkey oak-sessile oak (Quercus cerris and Q. petraea) stands. The study was carried out in the BĂŒkk mountains, Hungary. 122 sampling plots were established in 50-150 year old oak forests, where we studied the species composition and structure of the understorey and overstorey. The occurence of herbs was affected by canopy closure, the heterogenity and patchiness of the stand, the slope and the east-west component of the aspect. The composition of saplings was significantly explained by the ratio of the two major oak species in the stand and the proximity of the adult plants. An important result for forest management was that sessile oaks were able to regenerate almost only where they were dominant in the overstorey

    A test on Ellenberg indicator values in the Mediterranean evergreen woods (Quercetea ilicis)

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    The consistency and reliability of Ellenberg’s indicator values (Eiv) as ecological descriptors of the Mediterranean evergreen vegetation ascribed to the phytosociological class Quercetea ilicis have been checked on a set of 859 phytosociological relevĂ©s × 699 species. Diagnostic species were identified through a Twinspan analysis and their Eiv analyzed and related to the following independent variables: (1) annual mean temperatures, (2) annual rainfall. The results provided interesting insights to disentangle the current syntaxonomical framework at the alliance level demonstrating the usefulness of ecological indicator values to test the efficiency and predictivity of the phytosociological classification

    Seasonal water level fluctuations: Implications for reservoir limnology and management

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    With the purpose of finding out whether seasonal water level fluctuations could affect water quality in a reservoir subjected to those changes, trends in environmental variables and in phytoplankton and zooplankton assemblages were analysed. The reservoir’s hydrological cycle was characterized by three regimes. The maximum level phase lasted from January to the beginning of June, the emptying phase existed between mid-June to the beginning of September and the minimum level phase lasted from mid-September to the beginning of the first autumn/winter rain events. The highest values of total phosphorus, soluble reactive phosphorus, nitrate, water colour and chlorophyll a were found during the minimum level phase. The phytoplankton assemblage was dominated by taxa typical of meso-eutrophic environments during the emptying and minimum level phases. However, during the maximum level phase, taxa generally found in more oligotrophic systems were observed here also. Similar to other disturbed systems, the zooplankton assemblage was dominated by Rotifera, except in summer and autumn when the cladoceran Ceriodaphnia quadrangula and/or the copepod Tropocyclops prasinus became dominant. Although those shifts seem to be related to water level variations, further research is needed to evaluate to what extent they might also be induced by other seasonal factors acting independently of water fluctuations. Based upon the obtained data, suggestions for reservoir management are proposed

    PIT telemetry as a method to study the habitat requirements of fish populations: application to native and stocked trout movements

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    Passive integrated transponder (PIT) technology was used to study the behaviour of fishes during the summer season in two headwater streams of northeastern Portugal. A total of 71 PIT tags (12 mm long x 2.1 mm diameter) were surgically implanted in 1+ stocked (39) and native (32) brown trout of two size classes (< 20.0 and ≄ 20.0 cm). Eight independent antennae, connected to a multi-point decoder (MPD reader) unit, were placed in different microhabitats, selected randomly every three days during the observation period (29 August to 9 September in Baceiro stream and 19 September to 4 October in Sabor stream). The results confirmed this method as a suitable labour efficient tool to assess the movement and habitat use of sympatric stocked and native trout populations. About 76.9% of stocked and 59.4% of native PIT tagged trouts were detected. Multivariate techniques (CCA, DFA and classification tree) showed a separation in habitat use between the two sympatric populations. Stocked trout mainly used the microhabitats located in the middle of the channel with higher depths and without cover. Furthermore, these fishes displayed a greater mobility and a diel activity pattern different to native trout populations

    Gene Regulatory Networks from Multifactorial Perturbations Using Graphical Lasso: Application to the DREAM4 Challenge

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    A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network

    Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

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    Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation (rÂŻS) between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate (rÂŻS = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs
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